BackgroundBread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines.ResultsA sample of 62 diverse lines was re-sequenced using the whole exome capture and genotyping-by-sequencing approaches. We describe the allele frequency, functional significance, and chromosomal distribution of 1.57 million single nucleotide polymorphisms and 161,719 small indels. Our results suggest that duplicated homoeologous genes are under purifying selection. We find contrasting patterns of variation and inter-variant associations among wheat genomes; this, in addition to demographic factors, could be explained by differences in the effect of directional selection on duplicated homoeologs. Only a small fraction of the homoeologous regions harboring selected variants overlapped among the wheat genomes in any given wheat line. These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies.ConclusionsEvidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs. Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection. We hypothesize that allopolyploidy may have increased the likelihood of beneficial allele recovery by broadening the set of possible selection targets.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0606-4) contains supplementary material, which is available to authorized users.

Summary One of the major challenges for plant scientists is increasing wheat ( Triticum aestivum ) yield potential ( YP ). A significant bottleneck for increasing YP is achieving increased biomass through optimization of radiation use efficiency ( RUE ) along the crop cycle. Exotic material such as landraces and synthetic wheat has been incorporated into breeding programmes in an attempt to alleviate this; however, their contribution to YP is still unclear. To understand the genetic basis of biomass accumulation and RUE , we applied genome‐wide association study ( GWAS ) to a panel of 150 elite spring wheat genotypes including many landrace and synthetically derived lines. The panel was evaluated for 31 traits over 2 years under optimal growing conditions and genotyped using the 35K wheat breeders array. Marker‐trait association identified 94 SNP s significantly associated with yield, agronomic and phenology‐related traits along with RUE and final biomass ( BM _ PM ) at various growth stages that explained 7%–17% of phenotypic variation. Common SNP markers were identified for grain yield, BM _ PM and RUE on chromosomes 5A and 7A. Additionally, landrace and synthetic derivative lines showed higher thousand grain weight ( TGW ), BM _ PM and RUE but lower grain number ( GM 2) and harvest index ( HI ). Our work demonstrates the use of exotic material as a valuable resource to increase YP . It also provides markers for use in marker‐assisted breeding to systematically increase BM _ PM , RUE and TGW and avoid the TGW / GM 2 and BM _ PM / HI trade‐off. Thus, achieving greater genetic gains in elite germplasm while also highlighting genomic regions and candidate genes for further study.

BackgroundDNA methylation is an important mechanism of epigenetic gene expression control that can be passed between generations. Here, we use sodium bisulfite treatment and targeted gene enrichment to study genome-wide methylation across the three sub-genomes of allohexaploid wheat.ResultsWhile the majority of methylation is conserved across all three genomes we demonstrate that differential methylation exists between the sub-genomes in approximately equal proportions. We correlate sub-genome-specific promoter methylation with decreased expression levels and show that altered growing temperature has a small effect on methylation state, identifying a small but functionally relevant set of methylated genes. Finally, we demonstrate long-term methylation maintenance using a comparison between the D sub-genome of hexaploid wheat and its progenitor Aegilops tauschii.ConclusionsWe show that tri-genome methylation is highly conserved with the diploid wheat progenitor while sub-genome-specific methylation shows more variation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0838-3) contains supplementary material, which is available to authorized users.

Background Sequence exchange between homologous chromosomes through crossing over and gene conversion is highly conserved among eukaryotes, contributing to genome stability and genetic diversity. A lack of recombination limits breeding efforts in crops; therefore, increasing recombination rates can reduce linkage drag and generate new genetic combinations. Results We use computational analysis of 13 recombinant inbred mapping populations to assess crossover and gene conversion frequency in the hexaploid genome of wheat ( Triticum aestivum ). We observe that high-frequency crossover sites are shared between populations and that closely related parents lead to populations with more similar crossover patterns. We demonstrate that gene conversion is more prevalent and covers more of the genome in wheat than in other plants, making it a critical process in the generation of new haplotypes, particularly in centromeric regions where crossovers are rare. We identify quantitative trait loci for altered gene conversion and crossover frequency and confirm functionality for a novel RecQ helicase gene that belongs to an ancient clade that is missing in some plant lineages including Arabidopsis. Conclusions This is the first gene to be demonstrated to be involved in gene conversion in wheat. Harnessing the RecQ helicase has the potential to break linkage drag utilizing widespread gene conversions. Electronic supplementary material The online version of this article (10.1186/s13059-019-1675-6) contains supplementary material, which is available to authorized users.

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome’s role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.